The present invention relates to analyzing a process for the purpose of controlling and reducing the occurrence of faults, and, more particularly, to controlling process faults by performing a statistical analysis of collected process parameter values.
Many manufacturing processes suffer from parameter-related faults. A process parameter is a measurable and/or controllable aspect of the process (such as equipment temperature, pressure, material compositions, etc. . . . ) A parameter-related fault is a failure of the process which is linked to the values of the process parameters during a time period prior to the occurrence of a fault, but not necessarily in a deterministic or direct manner. In many manufacturing processes this time period is quite lengthy, on the order of minutes to hours, so that conventional feedback control methods are ineffective. In order to prevent or delay such a fault, it is necessary to identify problematic constellations of parameter values well before there is any evidence that a fault is likely to occur in the near future.
For example, the paper manufacturing process suffers from paper break faults, which are one of the most disruptive and costly problems in the paper manufacturing industry. Paper breaks result in many hours of lost production, process upsets, reduced reliability and significant loss of revenue potential. There is a considerable value and high demand for methods to reduce the number of breaks.
Early detection of an impeding paper break would allow operators to take corrective actions before the break occurs. Prediction models for paper breaks are difficult to build due to the complexity of the production process and the many variables involved in the process. Most paper mills have sensors and measurements on the production line that generate and store manufacturing data. However the collected data must be analyzed and evaluated in order to form predictions of the future process status.
One main goal of on-line process assessment is to determine the current probability that a process fault will occur within a certain upcoming time interval. In other words, what is the expected frequency of a particular failure under the given process conditions?
For illustration, consider a case in which a vehicle tire is not yet punctured, but factors including the tire's wear-and-tear record, the in-tire pressure and the road conditions make the puncture highly probable within the nearest N kilometers. We want to determine the probability for tire puncture within the next N kilometers under the current conditions. That is, what is the probability of tire puncture for a specific tire, under a known pressure, and driving with specific road conditions? Under different conditions, for example on a different road, the probability may be different.
An effective fault control system will, in addition to assessing the probability of an upcoming fault, issue prompts to the process operator suggesting how the probability may be reduced (e.g. change temperatures, materials, etc). Even though the failure is eventually inevitable, the system helps decrease the probability (i.e. reduce the frequency) of this type of failure, thus reducing the consequent losses.
There is thus a widely recognized need for, and it would be highly advantageous to have, a process analysis apparatus and method devoid of the above limitations.